https://github.com/sarthak-1408/rain-fall-prediction

This repository represents the End to End Machine Learning Project (Rain Fall Prediction in Australia).
https://github.com/sarthak-1408/rain-fall-prediction

heroku heroku-deployment machine-learning numpy pandas rain-fall rain-fall-prediction scikit-learn xgboost-algorithm

Last synced: about 2 months ago
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This repository represents the End to End Machine Learning Project (Rain Fall Prediction in Australia).

• Host: GitHub
• URL: https://github.com/sarthak-1408/rain-fall-prediction
• Owner: Sarthak-1408
• Created: 2021-06-01T12:51:51.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2023-02-22T18:18:25.000Z (almost 2 years ago)
• Last Synced: 2023-07-28T16:24:38.489Z (over 1 year ago)
• Topics: heroku, heroku-deployment, machine-learning, numpy, pandas, rain-fall, rain-fall-prediction, scikit-learn, xgboost-algorithm
• Language: Jupyter Notebook
• Homepage:
• Size: 5.99 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Rain Fall Prediction

Demo Video :point_down:

https://user-images.githubusercontent.com/72247049/120341601-a500fe00-c314-11eb-8ebd-2ecbd9463161.mp4

Demo Image :point_down:

![Screenshot (30)](https://user-images.githubusercontent.com/72247049/120343666-8865c580-c316-11eb-9cdf-a78ae4b0bb81.png)

![Screenshot (31)](https://user-images.githubusercontent.com/72247049/120343991-ccf16100-c316-11eb-8154-8fc5810695dd.png)

 - This repository represents **"Rain Fall Prediction in Australia"**.

 - With the help of this project we can Predict next-day rain in Australia.

 

 # Description :clipboard:

 

  - This Project is helps a lot of person who can check tomorrow's rain fall in Australia.

  - This project is tested over lot of ml models like xgboost, random forest, Gradient Boost Classifier, Decision Tree Classifier etc.. 

  Out of these models RainForestClassifier and XGBoostClassifier Perform very well giving an Accuracy score around 91 % .

  - Here due to my system compatibility is very low. So I havent done hyperparameter tuning. But it is highly recommended to do it if possible.

  - This project is totally based on **Machine Learning** and with the help of **Streamlit** library i can Build a Frontend.

  

  

 # Dataset :bulb:

  - Download the Dataset here :point_down:

  - ["https://www.kaggle.com/jsphyg/weather-dataset-rattle-package"](https://www.kaggle.com/jsphyg/weather-dataset-rattle-package)

  

  

  # Installation :desktop_computer:

  - Python 3.8+

  - Streamlit==0.73.1

  - scikit-learn==0.24.1

  - pandas==1.1.3

  - numpy==1.18.5

  - seaborn==0.11.1

  - missingno==0.4.2

  - imblearn==0.8.0

  - xgboost==1.4.1

  - anaconda (latest version)

  

 To install the required packages and libraries, run this command in the project directory after cloning the repository:

 

 ```

 pip install -r requirements.txt

```

  # Setup :computer:

 - First create a virtual environment by using this command:

 - conda create -n myenv python=3.6

 - Activate the environment using the below command:

 - conda activate myenv

 - Then install all the packages by using the following command

 - pip install -r requirements.txt

 - Now for the final step. Run the app

 - python app.py

  # Model Deployment

 - Model is Deployed using Streamlit library at Heroku server :point_down:

 

   https://sarthak-1408-rain-fall-prediction-app-fz64o1.streamlit.app/

   

 ## If you have any query regarding this project then contact me on the handles given below

 - **Linkdin** - https://www.linkedin.com/in/sarthak-sharma-5472aa1a0/

 - **Gmail** - [email protected]